Pre-mRNA splicing is essential for proper gene expression in higher eukaryotic genomes, as the vast majority of genes contain introns that have to be accurately recognized and removed. Recent studies have revealed that >90% of the genes undergo alternative splicing, which is believed to contribute to the complexity of the proteome in different cell types and tissues in vertebrates and abundant evidence suggests that altered splicing causes a variety of human diseases. Despite extensive knowledge on the splicing mechanism based on biochemical dissection of model minigenes, we know little about how many genes are involved in the regulation of alternative splicing and where the functional RNA elements are embedded in the human genome. Built on our productive research in the current award period, we now propose a bold plan to systematically attack the critical gap of our knowledge about the regulation of alternative splicing. We will pursue three major lines of research by utilizing the latest and innovative genomics technologies. (1) We will use a new, automated platform recently developed in our lab to profile hundreds of conserved alternative splicing events against every annotated genes in the human genome. This unbiased approach will generate unprecedented information to uncover novel splicing regulators and deduce pathways in regulated splicing. (2) We will focus on RNA binding proteins involved in individual regulatory pathways to elucidate the molecular basis for regulated splicing by mapping their physical interactions with expressed RNA. For this purpose, we will construct a large panel of cell lines based on FLP-In 293 cells to express individual RNA binding proteins as a V5-tagged protein at the C- terminus, which will permit large-scale mapping of RNA-protein interactions by CLIP-seq (CrossLinking ImmunoPrecipitation followed by high throughput sequencing) under a similar and optimized set of conditions. (3) Our third goal is to use the information generated from the proposed mapping and functional studies to develop an integrated framework for de novo prediction of splicing regulation by using machine-learning and graphical models. This research has the potential to fundamentally change our view on splicing control and its contribution to human disease.